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Doctor of Philosophy with a Major in Building Construction
Doctor of Philosophy with a Major in Building Construction
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ItemDECISION SUPPORT FRAMEWORK FOR TRANSFORMING URBAN BUILDINGS AT MULTIPLE SCALES(Georgia Institute of Technology, 2020-04-25) Chang, SoowonDue to the increasing population, cities are requiring more energy. Among urban elements, buildings account for about 40% of energy demands and 30% of carbon dioxide emissions globally. To address the increase of energy demands and environmental responsibility, existing buildings should be transformed into highly energy efficient forms. This research explores how to support decisions that affect performance-driven smart and resilient urban systems focusing on building renovations. The research scope covers the redevelopment of existing built forms at multiple scales. Since urban objects influence urban patterns at other scales, both individual and collective performances of buildings at larger scales should be evaluated to support proper redevelopment decisions. In addition, the transformation of existing buildings will encounter different problems and challenges at different scales in urban areas. On an individual building level, the selection of different envelope options can project the future architectural environment of buildings. On a block level, the performance will be changed along with combinations of building typologies such as land use, height, floor area, etc., and therefore changes to building typologies should be managed collectively to improve the performance. When PV are applied in buildings and hourly electricity demands are recognized, the dynamic energy flows on a community level will become complex to manage. In this respect, this research is devised to identify and address redevelopment problems at different scales: individual buildings, block, and community. On the individual building level, this research studies how to support decision-making when optimizing the selection of building envelopes by using a Genetic Algorithm (GA). Based on the findings from optimizing at each scale, an interdependence of building parameters and multiple performance is observed. Therefore, decision frameworks across multiple scales are extrapolated to support community-driven and building-driven decisions. On the block level, this research explores how existing building typologies influence multiple performance indicators in a collective manner to support reconfiguring decisions using a Bayesian Multilevel Modeling. On the community level, this study addresses how the community can optimize block boundaries for resiliently managing the energy demand and supply of groups of buildings by using K-nearest neighbors (KNN) and community clustering algorithms. This research will contribute to making appropriate decisions for investment, regulations, or guidelines when renovating physical building assets at different scales in urban areas. The research findings will consolidate theoretical understandings about the relationships between building design and construction parameters considering multiple performance indicators at multiple scales in urban areas. Since many cities are at the tipping point trying to become more resilient, increasingly focusing on sustainability, economic feasibility, and human well-being, a better understanding of the impact of built forms at multiple scales will support urban development decisions for the future smart and connected communities.
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ItemClash Resolution Optimization based on Component and Clash Dependent Networks(Georgia Institute of Technology, 2020-04-25) Hu, YuqingEffective coordination across multi-disciplines is crucial to make sure that the locations of building components meet physical and functional constraints. Building information modeling (BIM) has been increasingly applied for coordination and one of its most widely used applications is automatic clash detection. The realistic visualization function of BIM helps reduce ambiguity and expedites clash detection. However, many project participants criticize automatic clash detection, as many detected clashes are irrelevant with no significant impact on design or construction work, thereby decreasing the precision of clash results and the benefits of BIM. In addition, clash detection consists of discovering problems, but it does not entail solving these clashes. Even though some studies discussed automatic clash detection, they rarely discussed the dependence relationships between building components. However, a building is an inseparable whole, and the dependent relationships among building components propagate the impact of clashes. Relocating one object to correct one clash may result in other objects violating spatial constraints, which may directly cause new clashes or indirectly cause them through relocating other components. Therefore, figuring out the dependency among clash objects with peripheral building components is useful to optimizing clash solutions by avoiding change propagation. Algorithms are designed to automatically capture dependency relations from models to construct a component dependency network. The network is used as an input to distinguish irrelevant clashes for improving clash detection quality by analyzing the relations between clash components and the relations between clash components with their nearby components. The feasibility to harness the clash component network and graph theory are also explored to generate the clash component change list for minimizing clash change impact from a holistic perspective. In addition, this study demonstrates how to use BIM information to refine clash management, and specifically focus on designing a hybrid clash correction sequence to minimize potential iterative adjustments. The contributions of this study exist at three levels. The most straightforward contribution is that this research proposed a method to improve clash detection quality as well as to provide decision support for clash resolution, which can help project teams to focus on important clashes and improve design coordination efficiency. In addition, this research proposes a new perspective to view clashes, switching the clash management focus and inspiring researchers to focus on finding global optimal solutions for all clashes other than a single clash. The third level is that even though this research focuses on clash management, the optimization algorithms based on graph theory can be used in other interdependent systems to improve design and construction performance.
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ItemSustainable energy technology, adoption, rebound, and resilience(Georgia Institute of Technology, 2019-01-22) Hashemi Toroghi, ShahaboddinWhile in the United States, centralized generation and distribution network are the basis of the current electric infrastructure, the recent surge in uptake of solar photovoltaic (PV) systems introduces a new avenue to decentralize this system. Furthermore, PV systems can substitute the grid electricity and increase the share of renewable energy sources. While by 2018, five states in the U.S. (California, Hawaii, Nevada Massachusetts, and Vermont) could reach 10% threshold for the share of solar sources in generating electricity, at the country level this share is still less than 3%; whereas in some other countries, such as Germany and Japan, it has already reached more than 6%. This dissertation examines the diffusion of PV systems from three perspectives, addressing three gaps in knowledge: an empirical study of the diffusion of PV systems in Georgia, a method to estimate renewable rebound effect, and a framework to quantify the resilience capacity of an electric infrastructure system with emergency electricity generators, including PV systems. Three studies present the primary contributions of this research. Study 1 examines the diffusion of PV systems in Georgia, identifies characteristics of adopters and patterns of adoption, and forecasts the future adoption of PV systems. Study 2 introduces a new approach to estimate the direct rebound effect, subsequent of a major adoption of PV systems. Study 3 presents a state-of-the-art framework that quantifies the resilience capacity of an electric infrastructure system with emergency electricity generators. The findings of the study 1 provides a benchmark for the future adoption of PV systems and highlights the impact of socio-economic and location-based factors in the diffusion of PV systems in Georgia. These findings can be used to shape a more effective policy, aiming to increase the share of PV systems, or evaluate the effectiveness of a policy. The finding of the study 2 opens a new avenue to compute the rebound effect and can support development of a policy to mitigate the renewable rebound effect in a targeted region. The finding of the study 3 can help system designers to customize the design of a resilient system based on its characteristics. The introduced framework can further be used to investigate improvement of the resilience capacity in an electric infrastructure system by increasing the penetration of PV systems, or other decentralized electricity generators in a region.
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ItemIf these walls could talk: Automated performance measurement for building modeling decisions using data analytics(Georgia Institute of Technology, 2018-01-10) Yarmohammadi, SamanBuilding information modeling (BIM) is instrumental in documenting design, enhancing customer experience, and improving product functionality in capital projects. However, high-quality building models do not happen by accident, but rather because of a managed process that involves several participants from different disciplines and backgrounds. Throughout this process, the different priorities of design modelers often result in conflicts that can negatively impact project outcomes. There is a need for effective management of the modeling process to prevent such unwanted outcomes. Effective management of this process requires an ability to closely monitor the modeling process and correctly measure the modelers' performance. Nevertheless, existing methods of performance monitoring in building design practices lack an objective measurement system to quantify modeling progress. The widespread utilization of BIM tools presents a unique opportunity to retrieve granular design process data and conduct accurate performance measurements. This research improves upon previous efforts by presenting a novel application programming interface (API)-enabled approach to automatically collect detailed design development data directly from BIM software packages and efficiently calculate several modeling performance measures. The primary objective of this research is to create and examine the feasibility of a proposed automated design performance monitoring framework. The proposed framework provides the following capabilities: (a) non-intrusive and cost-effective data acquisition for capturing design development events in real time, (b) scalable and high-speed ingestion for the storage of design modeling data, (c) objective measurement of designer performance and estimating levels of effort required to complete design tasks, and (d) identifying optimal design teams using empirical performance information. In chapter 3, the utilization of modeling development information embedded in design log files that are produced by Autodesk Revit is proposed as a rich source of performance data. To this end, generalized suffix tree (GST) data structures are utilized to find common, frequent command sequences among Revit users. In addition to identifying the common command execution patterns, the average time it takes the selected modelers to execute command sequences is calculated. The obtained results demonstrate that there is a statistically significant difference between the modelers in terms of the time it takes them to conduct similar modeling tasks. Chapter 4 utilizes modeling software solution’s APIs to automatically collect and store timestamped design development information. The proposed passive data recording approach allows for the real-time capture of comprehensive user interface (UI) interaction and model element modification events. The proposed framework is also implemented as an Autodesk Revit plugin. An experiment is then conducted to verify the accuracy of this plugin. Throughout this experiment, manual recordings of model development events were compared against the automatically generated plugin output. Chapter 5 outlines the details of an approach to identify the optimal design modeling team configuration based on automatically collected performance data. To this end, an experiment is conducted to capture data using the developed Revit plugin. Experiment participants’ individual production rates are estimated to establish the validity of the proposed approach to identify the optimal design team configurations. The presented approach uses the earliest due date (EDD) sequencing rule in combination with the critical path method (CPM) to calculate the maximum lateness for different design team arrangements. The primary contributions of this study to the state of knowledge are as follows: (a) proposing a tailored string mining algorithm that is capable of extracting meaningful information from timestamped design development data, (b) developing a framework based on APIs to automatically collect design modeling data, and (c) creating a mathematical model to estimate design modeling project completion times based on individual performance data and project requirements. This study contributes to the state of practice by (a) allowing design project managers to gain an unprecedented insight into the evolution of a building model using the information embedded in design log files, (b) helping design managers to acquire progress information without the need to manually record and report data, and (c) enabling design managers to identify an optimal modeling team arrangement based on automatically captured, quantitative performance information.
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ItemAnalyzing uncertainty in the price of materials and financial risk management strategies(Georgia Institute of Technology, 2017-05-11) Ilbeigi, MohammadSignificant volatility and unprecedented uncertainty in the price of asphalt cement is a serious challenge for both contractors and state DOTs with regards to proper cost estimating and budgeting of transportation projects. Previous studies indicate that owner organizations often overpay for projects under fixed-price contracts that transfer the material price risk to contractors due to increased risk premiums and hidden contingencies in contractors’ submitted bid prices. A common method widely used by state DOTs for handling the issue of extra risk premiums in submitted bid prices and avoiding overpayment to contractors is to offer price adjustment clauses (PACs) in contracts. A PAC is a risk sharing contractual mechanism that guarantees an adjustment in payment to contractors based on the size and direction of the material price change. Although uncertainty in the price of asphalt cement is a serious challenge for both contractors and state DOTs and many transportation agencies utilize PACs to control consequences of material price volatility, there is little knowledge about analyzing uncertainties in the price of asphalt cement and actual performance of PACs. This dissertation aims to analyze uncertainty in the price of asphalt cement and examine performance of PACs in highway construction projects. After a comprehensive review of the existing body of knowledge about uncertainties in the price of critical materials in transportation projects and PACs, time series analysis is conducted and four univariate time series forecasting models are created to forecast future price of asphalt cement. The results of the time series forecasting show that all four time series models can predict the future values of asphalt cement price with proper accuracy but among the four models, the ARIMA and Holt Exponential Smoothing models are the most accurate prediction models with less than 2% error. Then, ARCH/GARCH time series analysis is conducted to quantify and forecast level of uncertainties in the price of asphalt cement. The results of this step can help transportation agencies systematically measure, analyze and forecast the uncertainties in the price of asphalt cement and implement their risk management strategies at the right time. In next step, impacts of offering PACs on submitted bid prices for major asphalt line items are analyzed using multivariate regression analysis. Finally, effects of offering PACs on dispersion of submitted bid prices and number of bidders are analyzed using system monitoring processes.
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ItemStakeholder alignment strategies for highway infrastructure public-private partnerships(Georgia Institute of Technology, 2017-03-31) Mostaan, KiaThe U.S. Department of Transportation (U.S. DOT) and state DOTs across the nation seek private investments to leverage their shrinking financial resources. Involvement of the private sector in financing and delivery of highway public-private partnerships (P3s) in the United States has experienced various limitations and challenges. The lack of standard approaches for P3 project delivery as well as public agencies’ varying levels of maturity in P3 implementation have negative impacts on successful project delivery. There is a need for research to determine the variability in public sector’s project delivery practice, due to its negative impacts that lead to market inefficiency and unpredictability. It is necessary to evaluate and analyze improvement strategies that can standardize P3 project delivery and enhance partnership alignment between the public and private sectors. The overarching objective of this study is to propose recommendations and enablers for improving alignment of public and private sectors in P3s. This study employs a three-phase combinatory research approach to achieve the research objectives. At first, a national survey of state DOTs is conducted to determine the degree of variability in public sector’s P3 practice. Following the public sector survey, twenty-five P3 experts are identified and selected from organizations that are active in the U.S. P3 market. A structured interview protocol is utilized to conduct interviews consistent with study questions and document the results. The third and final phase of the study methodology prior to concluding the analysis and providing recommendations is to conduct case studies of three mature P3 programs (Florida, Texas, and Virginia DOTs). The final phase of the research methodology aims to demonstrate best practices for P3 implementation and sustainment through case studies of agencies in the United States. While there is ample research on P3s in general, this study focuses on the alignment of public and private sectors in highway P3s. This study identifies the leading factors and issues that affect P3 decision-making by the public sector and the inconsistency in P3 implementation across project phases. This study also determines and evaluates the factors that can influence the public and private sector alignment in U.S. P3s and compares them with international best practices. Finally, by identifying recommended strategies and enabling mechanisms, this research aims to mitigate the lack of alignment between the public and private sectors in the U.S. P3 market. This study also demonstrates how mature P3 programs in the U.S. have achieved sustained partnerships. The final contribution of this study is a set of detailed recommendations for alignment of public and private sectors in U.S. P3s. The findings of this study are relevant for the U.S. P3 market, but may also be useful for planners and policy-makers in other countries. The major stakeholders impacted by this research involve public sector agencies, such as state DOTs, state and national infrastructure banks, metropolitan planning organizations (MPOs), permitting agencies and private sector stakeholders, such as multinational development companies, contractors, investments banks, procurement, financial and legal advisors.
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ItemDecision support system for masonry labor planning and allocation considering productivity and social sustainability(Georgia Institute of Technology, 2015-11-16) Florez, LauraMasonry construction is labor-intensive. Processes involve little to no mechanization and require a large number of crews made up of workers with diverse skills, capabilities, and personalities. Relationships among crews are tight and very dependent. Often crews are re-assembled and the superintendent is responsible for assigning workers to crews and allocating crews to different tasks to maximize workflow. This dynamic environment can influence the motivation of workers and impose pressure and stress on them. Workers, unlike other resources, have their own needs and requirements beyond the financial compensation for their work. Workers place a great value on requirements such as certainty about work assignments, matching assignments to career development goals, and work satisfaction. If managed properly, workers may bring considerable benefits to both the project and the contractor. A project that links workers to career goals not only allows contractors to develop more qualified staff for its future projects, but also gives the worker opportunities for career growth and development. Additionally, job satisfaction and efficiency increases from suitable worker assignment and consideration of tasks. Therefore, the study of sustainable labor management practices is of interest in masonry construction and other labor-intensive industries. A mixed-integer programming (MIP) model enables the integration of workers needs and contractor requirements into the process of labor allocation. Furthermore, the model can be used to quantify strategies that maximize productivity, quality of work, and the well-being of workers. Developing such a model is a necessary task. To plan and manage masonry construction, the contractor has to take into account not only multiple workers with different characteristics but also rules for crew design and makeup and project requirements in terms of personnel needs. Providing an analytical description of all the needs and requirements is challenging. Therefore, to determine labor management practices that indeed maximize production and maximize workers satisfaction, the model needs to realistically represent the realities in masonry construction sites and staffing practices, while remaining computationally manageable such that optimization models can be derived. This dissertation proposes a decision support system (DSS) for sustainable labor management in masonry construction that takes into consideration information on workers and job characteristics with the intention of assisting decision makers in allocating crews. Firstly, semi-structured interviews were conducted with masonry practitioners to gather perspectives on labor requirements, rules for crew design, and drivers for crew makeup. Secondly, a model that incorporates realities was implemented. The model supports masonry contractors and superintendent in the challenging process of managing crews, that is, to determine the composition of each crew and the allocation of crews to maximize productivity and workflow while considering workers’ preferences and well-being. With the DSS, project managers and superintendents are not only able to identify working patterns for each of the workers but also optimal crew formation and investment and labor costs. Data from real case study is used to compare the schedule and allocation on the site with the one proposed by the model. The comparison shows the model can optimize the allocation of crews to reduce the completion time to build the walls while maximizing the utilization of masons and outlining opportunities for concurrent work. It is expected that the DSS will help contractors improve productivity and quality while efficiently managing masonry workers in a more sustainable way. The contributions for the masonry industry are two-fold. Firstly, the proposed model considers a set of rules that masonry practitioners typically use to design crews of masons and analytically captures the realities of masonry construction jobsites when managing labor. Secondly, it attempts to quantify and mathematically model the practices that contractors use for crew makeup and evaluate labor management allocation both in terms of contractor requirements and worker needs. Literature review indicates that the existing models for labor allocation have not taken into consideration masonry site realities. An optimization framework, which combines masonry site realities from the semi-structured interviews is proposed. The framework results in a MIP model that is used to solve a crew scheduling and allocation problem. The model is formulated to determine which masons are in a crew and to assign crews to the different walls in a project. Additionally, it is used to evaluate crew design strategies that maximize productivity.
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ItemImpact to alternative contracting methods using multivariate analysis in the regulatory environment(Georgia Institute of Technology, 2008-06-24) Smith, Valerie Rose RieckeThis research addresses legislative impediments inherent to working in the government construction industry by investigating whether benefits exist when using alternative project delivery methods, and whether legislative limitations allowing the use of alternative project delivery methods impede any such benefits from being realized. The research begins by defining the project delivery method process, and explains in detail the four primary types and how they function. The research then provides a qualitative study that presents the perceived advantages and disadvantages of each method. Then, a second literature review provides an overview of previously published research in project delivery method selection, and examines federal and state legislative trends to establish the growing debate associated with alternative project delivery methods, focusing on the design-build method of project delivery. Finally, a quantitative analysis is presented to test whether federal and state legislative limitations influence the realization of any benefits of alternative project delivery methods, and specifically design-build, for federal projects. Project characteristics from the U.S. General Services Administration Capital Construction Project database are tested. The research suggests that when an alternative project delivery method, specifically design-build, is chosen, there are benefits in time and cost savings, and the ability to use the alternative project delivery method is affected by the removal of federal and state legislative impediments.